package industry_2024.industry_10.indicator

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.col

object indicator04 {
  def main(args: Array[String]): Unit = {
    /*
          编写scala代码，使用Spark根据hudi_gy_dwd层的fact_produce_record表，基于全量历史数据计算各设备生产一个产品的平均耗
          时，produce_code_end_time值为1900-01-01 00:00:00的数据为脏数据，需要剔除，并以produce_record_id和ProduceMachineID为联合主
          键进行去重（注：fact_produce_record表中，一条数据代表加工一个产品，produce_code_start_time字段为开始加工时间，produce_code_end_time字
          段为完成加工时间），将设备每个产品的耗时与该设备平均耗时作比较，保留耗时高于平均值的产品数据，将得到的数据写入ClickHouse数据
          库shtd_industry的machine_produce_per_avgtime表中（表结构如下），然后在Linux的ClickHouse命令行中根据设备id降序排序查询前3条数
          据，将SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将执行结果截图粘贴至客户端桌
          面【Release\任务B提交结果.docx】中对应的任务序号下
     */
    val spark=SparkSession.builder()
      .master("local[*]")
      .appName("指标计算第四题")
      .config("hive.exec.dynamic.partition.mode","nonstrict")
      .config("spark.serializer","org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions","org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .enableHiveSupport()
      .getOrCreate()

    val path="hdfs://192.168.40.110:9000/user/hive/warehouse/hudi_gy_dwd10.db/fact_produce_record"

    spark.read.format("hudi").load(path)
      .where(col("producecodeendtime")!=="1900-01-01 00:00:00")
      .dropDuplicates(Seq("producerecordid","producemachineid"))
      .createOrReplaceTempView("data")


    //  之所以
    val result=spark.sql(
      """
        |select
        |r1.producerecordid as produce_record_id,
        |r1.producemachineid as produce_machine_id,
        |r1.producetime,
        |ceil(avg(r1.producetime) over(partition by r1.producemachineid)) as produce_per_avgtime
        |from(
        |select
        |d.producerecordid,
        |d.producemachineid,
        |unix_timestamp(d.producecodeendtime) - unix_timestamp(d.producecodestarttime) as producetime
        |from data as d
        |) as r1
        |""".stripMargin)

    //  写入,表要自己建
    result.where(col("producetime") > col("produce_per_avgtime"))
      .dropDuplicates()
      .write.mode("append")
      .format("jdbc")
      .option("url","jdbc:clickhouse://192.168.40.110:8123/shtd_industry")
      .option("user","default")
      .option("password","")
      .option("driver","com.clickhouse.jdbc.ClickHouseDriver")
      .option("dbtable","machine_produce_per_avgtime10")
      .save()












    spark.close()
  }

}
